TensorBoard

Overview

The computations you’ll use TensorFlow for - like training a massive deep neural network - can be complex and confusing. To make it easier to understand, debug, and optimize TensorFlow programs, a suite of visualization tools called TensorBoard is available. You can use TensorBoard to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it.

For example, here’s a TensorBoard display for Keras accuracy and loss metrics:

Recording Data

The method for recording events for visualization by TensorBoard varies depending upon which TensorFlow interface you are working with:

Keras writes TensorBoard data at the end of each epoch so you won’t see any data in TensorBoard until 10-20 seconds after the end of the first epoch (TensorBoard automatically refreshes it’s display every 30 seconds during training).

tfruns

If you are using the tfruns package to track and manage training runs then there are some shortcuts available for the tensorboard() function: